{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9636e25e-d15f-4620-90fc-6959c188336e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.51861254 -0.56906261  0.26105088 ...  0.39346131  1.83381234\n",
      "   1.01300893]\n",
      " [ 0.24628963 -0.50234086 -0.90869274 ...  0.43875109  1.10735109\n",
      "   0.96524152]\n",
      " [ 0.19687903  0.05049647  1.22911457 ...  0.34817153  0.7860317\n",
      "   1.39514818]\n",
      " ...\n",
      " [ 0.33275817  1.88057869 -0.4246609  ... -1.64457872 -1.46320409\n",
      "   0.28057537]\n",
      " [ 0.20923168  0.26972507  0.01903496 ... -1.59928894 -1.37938164\n",
      "   0.29649784]\n",
      " [ 1.39508604  1.70900848  1.51146647 ... -1.55399916 -1.40732245\n",
      "  -0.59516041]]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn import preprocessing\n",
    "names=['label','a1','a2','a3','a4','a5','a6','a7','a8','a9','a10','a11','a12','a13']\n",
    "dataset=pd.read_csv(\"wine-clean.data\",names=names)\n",
    "data=dataset.iloc[range(0,178),range(1,14)]\n",
    "target=dataset.iloc[range(0,178),range(0,1)].values.reshape(1,178)[0]\n",
    "cdata=preprocessing.StandardScaler().fit_transform(data)\n",
    "print(cdata)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c14c8172-02ba-4f6f-a56b-35be652ab819",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "x,y=cdata,target\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,random_state=0)\n",
    "k_range=range(1,15)\n",
    "k_error=[]\n",
    "for k in k_range:\n",
    "    model=KNeighborsClassifier(n_neighbors=k)\n",
    "    scores=cross_val_score(model,x,y,cv=5,scoring='accuracy')\n",
    "    k_error.append(1-scores.mean())\n",
    "plt.rcParams['font.sans-serif']='SimHei'\n",
    "plt.plot(k_range,k_error,'r-')\n",
    "plt.xlabel('k的取值')\n",
    "plt.ylabel('预测误差率')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33a2ce52-bb72-48fa-9eb8-2f3418443d70",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:base] *",
   "language": "python",
   "name": "conda-base-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
